## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.548 4.854 7.271 9.825 10.160 132.499
## [1] 40758
# correlation plots
alpha = 0.05
ya_mat <- cor(select(ya_data, -record_id, -files, -matches("zscore|z_score"), trails_b_z_score, -Group))
ya_res <- cor.mtest(ya_mat, conf.level = (1-alpha))
corrplot(ya_mat, p.mat = ya_res$p, sig.level = alpha, insig = "blank", type = "upper")
oa_mat <- cor(select(oa_data, -record_id, -files, -Group))
oa_res <- cor.mtest(oa_mat, conf.level = (1-alpha))
corrplot(oa_mat, p.mat = oa_res$p, sig.level = alpha, insig = "blank", type = "upper")
#addCoef.col = TRUE, number.cex = .6
lm1 <- lm(md_mean_4 ~ age + actalph, data = oa_data)
summary(lm1)
##
## Call:
## lm(formula = md_mean_4 ~ age + actalph, data = oa_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.466e-05 -1.138e-05 -1.322e-06 1.020e-05 4.207e-05
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.783e-04 3.418e-05 11.068 1.09e-14 ***
## age 9.575e-07 5.001e-07 1.914 0.06167 .
## actalph -5.872e-05 1.950e-05 -3.011 0.00418 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.95e-05 on 47 degrees of freedom
## Multiple R-squared: 0.2337, Adjusted R-squared: 0.2011
## F-statistic: 7.167 on 2 and 47 DF, p-value: 0.00192
AIC(lm1)
## [1] -937.7315
d %>%
na.omit() %>%
select(Group, md_mean_3, md_mean_4, md_mean_5, actalph) %>%
melt(id.vars = c("Group", "actalph")) %>%
ggplot(aes(color = Group, group = Group)) +
geom_point(aes(x = actalph, y = value, group = Group, color = Group)) +
stat_smooth(aes(x = actalph, y = value, group = Group, color= Group), method = "lm") +
scale_color_manual(values = c("blue", "red")) +
facet_wrap(Group ~ variable, scales = "free_y") +
xlab("Width (alpha)") + ylab("Mean Diffusivity")
width (alpha) and width-ratio are related
lm2 <- lm(md_mean_4 ~ age + actwidthratio, data = oa_data)
summary(lm2)
##
## Call:
## lm(formula = md_mean_4 ~ age + actwidthratio, data = oa_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.475e-05 -1.109e-05 -1.818e-06 9.780e-06 4.240e-05
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.999e-04 4.603e-05 6.516 4.45e-08 ***
## age 9.539e-07 5.016e-07 1.902 0.06336 .
## actwidthratio 1.614e-04 5.446e-05 2.964 0.00475 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.954e-05 on 47 degrees of freedom
## Multiple R-squared: 0.2299, Adjusted R-squared: 0.1971
## F-statistic: 7.014 on 2 and 47 DF, p-value: 0.00216
d %>%
na.omit() %>%
select(Group, md_mean_3, md_mean_4, md_mean_5, actwidthratio) %>%
melt(id.vars = c("Group", "actwidthratio")) %>%
ggplot(aes(color = Group, group = Group)) +
geom_point(aes(x = actwidthratio, y = value, group = Group, color = Group)) +
stat_smooth(aes(x = actwidthratio, y = value, group = Group, color= Group), method = "lm") +
scale_color_manual(values = c("blue", "red")) +
facet_wrap(Group ~ variable, scales = "free_y") +
xlab("Width-ratio") + ylab("Mean Diffusivity")
lm3 <- lm(fa_mean_4 ~ age + actalph, data = oa_data)
summary(lm3) # *
##
## Call:
## lm(formula = fa_mean_4 ~ age + actalph, data = oa_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.08406 -0.01652 -0.00158 0.02077 0.05122
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.7187206 0.0565489 12.710 <2e-16 ***
## age -0.0010360 0.0008274 -1.252 0.2167
## actalph 0.0636989 0.0322615 1.974 0.0542 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03225 on 47 degrees of freedom
## Multiple R-squared: 0.1158, Adjusted R-squared: 0.07813
## F-statistic: 3.076 on 2 and 47 DF, p-value: 0.05552
d %>%
na.omit() %>%
select(Group, fa_mean_3, fa_mean_4, fa_mean_5, actalph) %>%
melt(id.vars = c("Group", "actalph")) %>%
ggplot() +
geom_point(aes(x = actalph, y = value, group = Group, color = Group)) +
stat_smooth(aes(x = actalph, y = value, group = Group, color= Group), method = "lm") +
scale_color_manual(values = c("blue", "red")) +
facet_wrap(Group ~ variable, scales = "free_y") +
xlab("Width (alpha)") + ylab("Fractional Anisotropy")
#older adults
#body - highest correlation values
#width-,age+,RA+NS
#longer durations of low activity predict greater MD
stepdata <- select(oa_data, age, IS:RA, actalph:fact, -rsqact, -actwidthratio, md_mean_4)
summary(slm02 <- lm(md_mean_4 ~ ., data = stepdata))
##
## Call:
## lm(formula = md_mean_4 ~ ., data = stepdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.415e-05 -1.024e-05 -3.880e-07 9.977e-06 4.158e-05
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.599e-04 4.791e-05 7.513 2.35e-09 ***
## age 8.583e-07 5.448e-07 1.575 0.12251
## IS 2.218e-06 3.293e-05 0.067 0.94660
## IV -6.015e-06 1.363e-05 -0.441 0.66115
## RA 3.779e-05 2.771e-05 1.364 0.17970
## actalph -6.511e-05 2.055e-05 -3.169 0.00282 **
## fact -1.314e-09 2.066e-09 -0.636 0.52810
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.964e-05 on 43 degrees of freedom
## Multiple R-squared: 0.2885, Adjusted R-squared: 0.1893
## F-statistic: 2.906 on 6 and 43 DF, p-value: 0.01812
slm2 <- step(slm02, direction = "both")
## Start: AIC=-1077.34
## md_mean_4 ~ age + IS + IV + RA + actalph + fact
##
## Df Sum of Sq RSS AIC
## - IS 1 1.8000e-12 1.6588e-08 -1079.3
## - IV 1 7.5100e-11 1.6661e-08 -1079.1
## - fact 1 1.5610e-10 1.6742e-08 -1078.9
## <none> 1.6586e-08 -1077.3
## - RA 1 7.1750e-10 1.7304e-08 -1077.2
## - age 1 9.5720e-10 1.7543e-08 -1076.5
## - actalph 1 3.8729e-09 2.0459e-08 -1068.8
##
## Step: AIC=-1079.33
## md_mean_4 ~ age + IV + RA + actalph + fact
##
## Df Sum of Sq RSS AIC
## - IV 1 7.3400e-11 1.6661e-08 -1081.1
## - fact 1 2.1620e-10 1.6804e-08 -1080.7
## <none> 1.6588e-08 -1079.3
## - RA 1 1.0590e-09 1.7647e-08 -1078.2
## - age 1 1.0722e-09 1.7660e-08 -1078.2
## + IS 1 1.8000e-12 1.6586e-08 -1077.3
## - actalph 1 3.9257e-09 2.0513e-08 -1070.7
##
## Step: AIC=-1081.11
## md_mean_4 ~ age + RA + actalph + fact
##
## Df Sum of Sq RSS AIC
## - fact 1 1.5260e-10 1.6814e-08 -1082.7
## <none> 1.6661e-08 -1081.1
## - age 1 1.1252e-09 1.7786e-08 -1079.8
## - RA 1 1.1973e-09 1.7858e-08 -1079.6
## + IV 1 7.3400e-11 1.6588e-08 -1079.3
## + IS 1 0.0000e+00 1.6661e-08 -1079.1
## - actalph 1 3.8573e-09 2.0518e-08 -1072.7
##
## Step: AIC=-1082.65
## md_mean_4 ~ age + RA + actalph
##
## Df Sum of Sq RSS AIC
## <none> 1.6814e-08 -1082.7
## - RA 1 1.0502e-09 1.7864e-08 -1081.6
## + fact 1 1.5260e-10 1.6661e-08 -1081.1
## + IS 1 5.4400e-11 1.6759e-08 -1080.8
## + IV 1 9.8000e-12 1.6804e-08 -1080.7
## - age 1 1.4206e-09 1.8234e-08 -1080.6
## - actalph 1 4.1180e-09 2.0932e-08 -1073.7
summary(slm2)
##
## Call:
## lm(formula = md_mean_4 ~ age + RA + actalph, data = stepdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.276e-05 -9.223e-06 -1.980e-06 1.014e-05 4.239e-05
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.438e-04 3.922e-05 8.767 2.25e-11 ***
## age 9.670e-07 4.905e-07 1.971 0.05471 .
## RA 3.636e-05 2.145e-05 1.695 0.09683 .
## actalph -6.563e-05 1.955e-05 -3.357 0.00159 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.912e-05 on 46 degrees of freedom
## Multiple R-squared: 0.2788, Adjusted R-squared: 0.2317
## F-statistic: 5.926 on 3 and 46 DF, p-value: 0.001662
AIC(slm2)
## [1] -938.7609
#higher width predicts higher FA
#width+
#longer durations of low activity predict lower FA
stepdata <- select(oa_data, age, IS:RA, actalph:fact, -rsqact, -actwidthratio, fa_mean_4)
summary(slm02 <- lm(fa_mean_4 ~ ., data = stepdata))
##
## Call:
## lm(formula = fa_mean_4 ~ ., data = stepdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.085845 -0.017658 -0.001079 0.018599 0.053401
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.207e-01 8.102e-02 8.896 2.66e-11 ***
## age -8.430e-04 9.213e-04 -0.915 0.3653
## IS 2.603e-03 5.568e-02 0.047 0.9629
## IV 3.504e-03 2.304e-02 0.152 0.8798
## RA -3.510e-02 4.685e-02 -0.749 0.4578
## actalph 6.462e-02 3.475e-02 1.860 0.0698 .
## fact 2.473e-06 3.493e-06 0.708 0.4827
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03321 on 43 degrees of freedom
## Multiple R-squared: 0.1424, Adjusted R-squared: 0.02269
## F-statistic: 1.19 on 6 and 43 DF, p-value: 0.33
slm2 <- step(slm02, direction = "both")
## Start: AIC=-334.03
## fa_mean_4 ~ age + IS + IV + RA + actalph + fact
##
## Df Sum of Sq RSS AIC
## - IS 1 0.0000024 0.047428 -336.03
## - IV 1 0.0000255 0.047451 -336.00
## - fact 1 0.0005530 0.047979 -335.45
## - RA 1 0.0006190 0.048045 -335.38
## - age 1 0.0009233 0.048349 -335.07
## <none> 0.047426 -334.03
## - actalph 1 0.0038148 0.051241 -332.16
##
## Step: AIC=-336.03
## fa_mean_4 ~ age + IV + RA + actalph + fact
##
## Df Sum of Sq RSS AIC
## - IV 1 0.0000282 0.047456 -338.00
## - RA 1 0.0008103 0.048238 -337.18
## - fact 1 0.0009461 0.048374 -337.04
## - age 1 0.0009791 0.048407 -337.01
## <none> 0.047428 -336.03
## - actalph 1 0.0039169 0.051345 -334.06
## + IS 1 0.0000024 0.047426 -334.03
##
## Step: AIC=-338
## fa_mean_4 ~ age + RA + actalph + fact
##
## Df Sum of Sq RSS AIC
## - RA 1 0.0008925 0.048349 -339.07
## - fact 1 0.0009685 0.048425 -338.99
## - age 1 0.0010127 0.048469 -338.94
## <none> 0.047456 -338.00
## - actalph 1 0.0038897 0.051346 -336.06
## + IV 1 0.0000282 0.047428 -336.03
## + IS 1 0.0000051 0.047451 -336.00
##
## Step: AIC=-339.07
## fa_mean_4 ~ age + actalph + fact
##
## Df Sum of Sq RSS AIC
## - fact 1 0.0005482 0.048897 -340.50
## - age 1 0.0011354 0.049484 -339.91
## <none> 0.048349 -339.07
## + RA 1 0.0008925 0.047456 -338.00
## - actalph 1 0.0034013 0.051750 -337.67
## + IS 1 0.0001872 0.048162 -337.26
## + IV 1 0.0001104 0.048238 -337.18
##
## Step: AIC=-340.5
## fa_mean_4 ~ age + actalph
##
## Df Sum of Sq RSS AIC
## - age 1 0.0016310 0.050528 -340.86
## <none> 0.048897 -340.50
## + fact 1 0.0005482 0.048349 -339.07
## + RA 1 0.0004721 0.048425 -338.99
## + IS 1 0.0000209 0.048876 -338.52
## - actalph 1 0.0040558 0.052953 -338.52
## + IV 1 0.0000006 0.048896 -338.50
##
## Step: AIC=-340.86
## fa_mean_4 ~ actalph
##
## Df Sum of Sq RSS AIC
## <none> 0.050528 -340.86
## + age 1 0.0016310 0.048897 -340.50
## + fact 1 0.0010438 0.049484 -339.91
## + RA 1 0.0004522 0.050076 -339.31
## + IS 1 0.0000038 0.050524 -338.87
## + IV 1 0.0000028 0.050525 -338.87
## - actalph 1 0.0047703 0.055298 -338.35
summary(slm2)
##
## Call:
## lm(formula = fa_mean_4 ~ actalph, data = stepdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.082204 -0.022673 0.000078 0.022388 0.054712
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.65044 0.01505 43.206 <2e-16 ***
## actalph 0.06858 0.03221 2.129 0.0384 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03244 on 48 degrees of freedom
## Multiple R-squared: 0.08626, Adjusted R-squared: 0.06723
## F-statistic: 4.532 on 1 and 48 DF, p-value: 0.03843
AIC(slm2)
## [1] -196.9686
#young adults
#body - highest correlation values
#IV-, RA+
stepdata <- select(ya_data, age, IS:RA, actalph:fact, -rsqact, -actwidthratio, md_mean_4)
summary(slm02y <- lm(md_mean_4 ~ ., data = stepdata))
##
## Call:
## lm(formula = md_mean_4 ~ ., data = stepdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.088e-05 -7.751e-06 -7.970e-07 9.215e-06 2.918e-05
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.381e-04 1.868e-05 23.458 <2e-16 ***
## age 1.912e-07 5.891e-07 0.325 0.7471
## IS 2.352e-05 2.871e-05 0.819 0.4175
## IV -2.430e-05 1.068e-05 -2.274 0.0283 *
## RA 2.220e-05 1.955e-05 1.135 0.2628
## actalph 2.529e-06 1.597e-05 0.158 0.8750
## fact -7.394e-10 1.401e-09 -0.528 0.6006
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.462e-05 on 41 degrees of freedom
## Multiple R-squared: 0.2718, Adjusted R-squared: 0.1652
## F-statistic: 2.551 on 6 and 41 DF, p-value: 0.03428
slm2y <- step(slm02y, direction = "both")
## Start: AIC=-1062.37
## md_mean_4 ~ age + IS + IV + RA + actalph + fact
##
## Df Sum of Sq RSS AIC
## - actalph 1 5.3500e-12 8.7643e-09 -1064.3
## - age 1 2.2510e-11 8.7814e-09 -1064.2
## - fact 1 5.9460e-11 8.8184e-09 -1064.0
## - IS 1 1.4334e-10 8.9023e-09 -1063.6
## - RA 1 2.7544e-10 9.0344e-09 -1062.9
## <none> 8.7589e-09 -1062.4
## - IV 1 1.1047e-09 9.8636e-09 -1058.7
##
## Step: AIC=-1064.34
## md_mean_4 ~ age + IS + IV + RA + fact
##
## Df Sum of Sq RSS AIC
## - age 1 2.1830e-11 8.7861e-09 -1066.2
## - fact 1 5.4950e-11 8.8192e-09 -1066.0
## - IS 1 1.5088e-10 8.9152e-09 -1065.5
## <none> 8.7643e-09 -1064.3
## - RA 1 3.8479e-10 9.1491e-09 -1064.3
## + actalph 1 5.3500e-12 8.7589e-09 -1062.4
## - IV 1 1.6067e-09 1.0371e-08 -1058.3
##
## Step: AIC=-1066.22
## md_mean_4 ~ IS + IV + RA + fact
##
## Df Sum of Sq RSS AIC
## - fact 1 4.3320e-11 8.8294e-09 -1068.0
## - IS 1 1.4407e-10 8.9302e-09 -1067.4
## <none> 8.7861e-09 -1066.2
## - RA 1 3.9516e-10 9.1813e-09 -1066.1
## + age 1 2.1830e-11 8.7643e-09 -1064.3
## + actalph 1 4.6700e-12 8.7814e-09 -1064.2
## - IV 1 1.5897e-09 1.0376e-08 -1060.2
##
## Step: AIC=-1067.99
## md_mean_4 ~ IS + IV + RA
##
## Df Sum of Sq RSS AIC
## - IS 1 1.1125e-10 8.9407e-09 -1069.4
## - RA 1 3.7091e-10 9.2003e-09 -1068.0
## <none> 8.8294e-09 -1068.0
## + fact 1 4.3320e-11 8.7861e-09 -1066.2
## + age 1 1.0200e-11 8.8192e-09 -1066.0
## + actalph 1 8.9000e-13 8.8285e-09 -1066.0
## - IV 1 1.5467e-09 1.0376e-08 -1062.2
##
## Step: AIC=-1069.39
## md_mean_4 ~ IV + RA
##
## Df Sum of Sq RSS AIC
## <none> 8.9407e-09 -1069.4
## + IS 1 1.1125e-10 8.8294e-09 -1068.0
## + actalph 1 1.3580e-11 8.9271e-09 -1067.5
## + fact 1 1.0510e-11 8.9302e-09 -1067.4
## + age 1 9.9400e-12 8.9307e-09 -1067.4
## - RA 1 1.0221e-09 9.9628e-09 -1066.2
## - IV 1 2.0437e-09 1.0984e-08 -1061.5
summary(slm2y)
##
## Call:
## lm(formula = md_mean_4 ~ IV + RA, data = stepdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.117e-05 -7.563e-06 2.260e-07 1.093e-05 2.956e-05
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.419e-04 1.360e-05 32.489 < 2e-16 ***
## IV -2.614e-05 8.151e-06 -3.207 0.00247 **
## RA 3.050e-05 1.345e-05 2.268 0.02817 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.41e-05 on 45 degrees of freedom
## Multiple R-squared: 0.2567, Adjusted R-squared: 0.2237
## F-statistic: 7.77 on 2 and 45 DF, p-value: 0.001263
#age+, fact+NS
stepdata <- select(ya_data, age, IS:RA, actalph:fact, -rsqact, -actwidthratio, fa_mean_4)
summary(slm02y <- lm(fa_mean_4 ~ ., data = stepdata))
##
## Call:
## lm(formula = fa_mean_4 ~ ., data = stepdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.041839 -0.009620 0.001603 0.009002 0.049126
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.166e-01 2.727e-02 22.612 <2e-16 ***
## age 1.655e-03 8.601e-04 1.924 0.0614 .
## IS 3.621e-02 4.192e-02 0.864 0.3928
## IV -2.837e-03 1.560e-02 -0.182 0.8566
## RA -3.559e-02 2.855e-02 -1.247 0.2196
## actalph -1.090e-02 2.332e-02 -0.467 0.6428
## fact 2.885e-06 2.046e-06 1.410 0.1662
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02134 on 41 degrees of freedom
## Multiple R-squared: 0.2018, Adjusted R-squared: 0.08496
## F-statistic: 1.727 on 6 and 41 DF, p-value: 0.139
slm2y <- step(slm02y, direction = "both")
## Start: AIC=-362.89
## fa_mean_4 ~ age + IS + IV + RA + actalph + fact
##
## Df Sum of Sq RSS AIC
## - IV 1 0.00001506 0.018686 -364.86
## - actalph 1 0.00009942 0.018771 -364.64
## - IS 1 0.00033975 0.019011 -364.03
## - RA 1 0.00070778 0.019379 -363.11
## <none> 0.018671 -362.89
## - fact 1 0.00090504 0.019576 -362.62
## - age 1 0.00168511 0.020356 -360.75
##
## Step: AIC=-364.86
## fa_mean_4 ~ age + IS + RA + actalph + fact
##
## Df Sum of Sq RSS AIC
## - actalph 1 0.00008641 0.018773 -366.63
## - IS 1 0.00049196 0.019178 -365.61
## <none> 0.018686 -364.86
## - RA 1 0.00091373 0.019600 -364.56
## - fact 1 0.00092213 0.019608 -364.54
## + IV 1 0.00001506 0.018671 -362.89
## - age 1 0.00167221 0.020359 -362.74
##
## Step: AIC=-366.63
## fa_mean_4 ~ age + IS + RA + fact
##
## Df Sum of Sq RSS AIC
## - IS 1 0.00069625 0.019469 -366.89
## <none> 0.018773 -366.63
## - fact 1 0.00083900 0.019612 -366.54
## - RA 1 0.00121672 0.019989 -365.62
## + actalph 1 0.00008641 0.018686 -364.86
## + IV 1 0.00000204 0.018771 -364.64
## - age 1 0.00178611 0.020559 -364.27
##
## Step: AIC=-366.89
## fa_mean_4 ~ age + RA + fact
##
## Df Sum of Sq RSS AIC
## - RA 1 0.00061567 0.020085 -367.39
## <none> 0.019469 -366.89
## + IS 1 0.00069625 0.018773 -366.63
## + actalph 1 0.00029070 0.019178 -365.61
## - age 1 0.00156272 0.021032 -365.18
## + IV 1 0.00002663 0.019442 -364.95
## - fact 1 0.00169389 0.021163 -364.88
##
## Step: AIC=-367.39
## fa_mean_4 ~ age + fact
##
## Df Sum of Sq RSS AIC
## <none> 0.020085 -367.39
## + RA 1 0.00061567 0.019469 -366.89
## - fact 1 0.00123287 0.021317 -366.53
## + actalph 1 0.00044599 0.019639 -366.47
## - age 1 0.00150106 0.021586 -365.93
## + IS 1 0.00009520 0.019989 -365.62
## + IV 1 0.00004744 0.020037 -365.51
summary(slm2y)
##
## Call:
## lm(formula = fa_mean_4 ~ age + fact, data = stepdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.04646 -0.01031 0.00089 0.01160 0.05280
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.066e-01 1.835e-02 33.054 <2e-16 ***
## age 1.527e-03 8.327e-04 1.834 0.0733 .
## fact 2.883e-06 1.735e-06 1.662 0.1035
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02113 on 45 degrees of freedom
## Multiple R-squared: 0.1413, Adjusted R-squared: 0.1032
## F-statistic: 3.704 on 2 and 45 DF, p-value: 0.03243
library(beset)
## Loading required package: foreach
##
## Attaching package: 'foreach'
## The following objects are masked from 'package:purrr':
##
## accumulate, when
## Loading required package: Matrix
##
## Attaching package: 'Matrix'
## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack
## Loading required package: splines
## Loading required package: stats4
stepdata <- select(oa_data, age, IS:RA, actalph:fact, -actwidthratio, fa_mean_4)
mod01 <- (slm02 <- lm(fa_mean_4 ~ ., data = stepdata, direction = "both"))
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
## extra argument 'direction' will be disregarded
summary(mod01)
##
## Call:
## lm(formula = fa_mean_4 ~ ., data = stepdata, direction = "both")
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.081489 -0.015179 -0.000762 0.019648 0.053460
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.171e-01 8.154e-02 8.795 4.48e-11 ***
## age -8.254e-04 9.261e-04 -0.891 0.3779
## IS 1.124e-02 5.708e-02 0.197 0.8449
## IV 5.458e-03 2.329e-02 0.234 0.8159
## RA -2.980e-03 6.307e-02 -0.047 0.9625
## actalph 6.470e-02 3.492e-02 1.853 0.0709 .
## rsqact -1.969e-01 2.573e-01 -0.765 0.4483
## fact 1.162e-05 1.245e-05 0.933 0.3562
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03337 on 42 degrees of freedom
## Multiple R-squared: 0.1542, Adjusted R-squared: 0.01319
## F-statistic: 1.094 on 7 and 42 DF, p-value: 0.3848
mod <- beset_lm(fa_mean_4 ~ ., data = stepdata, n_folds = 51, force_in = "age")
## Performing leave-one-out cross-validation
## NOTE: Repetitions of leave-one-out cross-validation
## are pointless and will not be performed.
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
plot(mod)
#return the model with the smallest number of parameters that is within one standard error of the model with the lowest cross-validation error (the “1-SE rule”).
summary(mod, n_folds = 51)
##
## =======================================================
## Best Model:
## ~ age
##
## Coefficients:
## Estimate
## (Intercept) 0.703800
## age -0.001233
##
## (Dispersion parameter for gaussian family taken to be 0.001103185)
##
## Log-likelihood: 100.3 on 3 Df
## AIC: -194.62
##
## Number of Fisher Scoring iterations: 2
##
## Train-sample R-squared = 0.04
## Cross-validated R-squared = -0.03
## =======================================================
#without 1-SE rule
summary(mod, oneSE = FALSE, n_folds = 51) #age, RA, alpha
##
## =======================================================
## Best Model:
## ~ age + actalph
##
## Coefficients:
## Estimate
## (Intercept) 0.718700
## age -0.001036
## actalph 0.063700
##
## (Dispersion parameter for gaussian family taken to be 0.001040363)
##
## Log-likelihood: 102.3 on 4 Df
## AIC: -196.61
##
## Number of Fisher Scoring iterations: 2
##
## Train-sample R-squared = 0.12
## Cross-validated R-squared = 0
## =======================================================
#aic
summary(mod, metric = "aic", n_folds = 51)
##
## =======================================================
## Best Model:
## ~ age + actalph
##
## Coefficients:
## Estimate
## (Intercept) 0.718700
## age -0.001036
## actalph 0.063700
##
## (Dispersion parameter for gaussian family taken to be 0.001040363)
##
## Log-likelihood: 102.3 on 4 Df
## AIC: -196.61
##
## Number of Fisher Scoring iterations: 2
##
## Train-sample R-squared = 0.12
## Cross-validated R-squared = 0
## =======================================================
library(beset)
stepdata <- select(oa_data, age, IS:RA, actalph:fact, -actwidthratio, md_mean_4)
mod <- beset_lm(md_mean_4 ~ ., data = stepdata, n_folds = 51, force_in = "age")
## Performing leave-one-out cross-validation
## NOTE: Repetitions of leave-one-out cross-validation
## are pointless and will not be performed.
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
plot(mod)
#return the model with the smallest number of parameters that is within one standard error of the model with the lowest cross-validation error (the “1-SE rule”).
summary(mod, n_folds = 51)
##
## =======================================================
## Best Model:
## ~ age
##
## Coefficients:
## Estimate
## (Intercept) 3.921e-04
## age 1.139e-06
##
## (Dispersion parameter for gaussian family taken to be 4.439699e-10)
##
## Log-likelihood: 468.5 on 3 Df
## AIC: -930.91
##
## Number of Fisher Scoring iterations: 2
##
## Train-sample R-squared = 0.09
## Cross-validated R-squared = 0.02
## =======================================================
#without 1-SE rule
summary(mod, oneSE = FALSE, n_folds = 51) #age, RA, alpha
##
## =======================================================
## Best Model:
## ~ age + RA + actalph
##
## Coefficients:
## Estimate
## (Intercept) 3.438e-04
## age 9.670e-07
## RA 3.636e-05
## actalph -6.563e-05
##
## (Dispersion parameter for gaussian family taken to be 3.655151e-10)
##
## Log-likelihood: 474.4 on 5 Df
## AIC: -938.76
##
## Number of Fisher Scoring iterations: 2
##
## Train-sample R-squared = 0.28
## Cross-validated R-squared = 0.16
## =======================================================
#aic
summary(mod, metric = "aic", n_folds = 51)
##
## =======================================================
## Best Model:
## ~ age + RA + actalph
##
## Coefficients:
## Estimate
## (Intercept) 3.438e-04
## age 9.670e-07
## RA 3.636e-05
## actalph -6.563e-05
##
## (Dispersion parameter for gaussian family taken to be 3.655151e-10)
##
## Log-likelihood: 474.4 on 5 Df
## AIC: -938.76
##
## Number of Fisher Scoring iterations: 2
##
## Train-sample R-squared = 0.28
## Cross-validated R-squared = 0.16
## =======================================================
# correlation plots
alpha = 0.05
d$ef <- (d$trails_b_z_score + d$ds_zscore)/2
oa_data <- filter(d, Group == "Older Adults")
oa_mat <- cor(select(oa_data, actalph, actwidthratio, ef, matches("zscore|z_score")))
oa_res <- cor.mtest(oa_mat, conf.level = (1-alpha))
corrplot(oa_mat, p.mat = oa_res$p, sig.level = alpha, insig = "blank", type = "upper")
oa_data <- filter(d, Group == "Older Adults")
oa_mat <- cor(select(oa_data, matches("fa_mean|md_mean"), matches("zscore|z_score")))
oa_res <- cor.mtest(oa_mat, conf.level = (1-alpha))
corrplot(oa_mat, p.mat = oa_res$p, sig.level = alpha, insig = "blank", type = "upper")
#addCoef.col = TRUE, number.cex = .6
summary(lm(fa_mean_4 ~ cowat_zscore + actalph, data = oa_data))
##
## Call:
## lm(formula = fa_mean_4 ~ cowat_zscore + actalph, data = oa_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.073588 -0.020417 -0.002005 0.018799 0.062497
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.641163 0.015051 42.599 <2e-16 ***
## cowat_zscore 0.010726 0.004805 2.232 0.0304 *
## actalph 0.054462 0.031594 1.724 0.0913 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03118 on 47 degrees of freedom
## Multiple R-squared: 0.1739, Adjusted R-squared: 0.1387
## F-statistic: 4.946 on 2 and 47 DF, p-value: 0.01124
ccvol <- read_delim("/Volumes/schnyer/Aging_DecMem/Scan_Data/BIDS/derivatives/freesurfer/aseg_table.txt", delim = "\t")
## Parsed with column specification:
## cols(
## .default = col_double(),
## `Measure:volume` = col_character()
## )
## See spec(...) for full column specifications.
head(ccvol)
## # A tibble: 6 x 27
## `Measure:volume` CC_Posterior CC_Mid_Posterior CC_Central CC_Mid_Anterior
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 sub-30004 873. 486 522. 477.
## 2 sub-30012 987. 559. 650. 566.
## 3 sub-30015 1137. 677. 726. 681.
## 4 sub-30019 1008. 563. 570. 399.
## 5 sub-30020 974. 416. 695. 496.
## 6 sub-30040 801. 426. 638. 534
## # … with 22 more variables: CC_Anterior <dbl>, BrainSegVol <dbl>,
## # BrainSegVolNotVent <dbl>, BrainSegVolNotVentSurf <dbl>, lhCortexVol <dbl>,
## # rhCortexVol <dbl>, CortexVol <dbl>, lhCerebralWhiteMatterVol <dbl>,
## # rhCerebralWhiteMatterVol <dbl>, CerebralWhiteMatterVol <dbl>,
## # SubCortGrayVol <dbl>, TotalGrayVol <dbl>, SupraTentorialVol <dbl>,
## # SupraTentorialVolNotVent <dbl>, SupraTentorialVolNotVentVox <dbl>,
## # MaskVol <dbl>, `BrainSegVol-to-eTIV` <dbl>, `MaskVol-to-eTIV` <dbl>,
## # lhSurfaceHoles <dbl>, rhSurfaceHoles <dbl>, SurfaceHoles <dbl>,
## # EstimatedTotalIntraCranialVol <dbl>
library(stringr)
ccvol$CC_Total <- ccvol$CC_Anterior + ccvol$CC_Central + ccvol$CC_Mid_Anterior + ccvol$CC_Mid_Posterior + ccvol$CC_Posterior
ccvol$record_id <- substr(ccvol$`Measure:volume`, 5, 9)
ccvol$record_id
## [1] "30004" "30012" "30015" "30019" "30020" "30040" "30057" "30066" "30074"
## [10] "30085" "30088" "30090" "30091" "30096" "30105" "30116" "30119" "30128"
## [19] "30181" "30217" "30236" "30283" "30330" "30346" "30376" "30395" "30400"
## [28] "30412" "30426" "30432" "30466" "30469" "30476" "30478" "30568" "30581"
## [37] "30584" "30588" "40160" "40170" "40175" "40351" "40490" "40496" "40500"
## [46] "40512" "40515" "40516" "40520" "40522" "40524" "40547" "40550" "40564"
## [55] "40601" "40608" "40619" "40623" "40649" "40650" "40655" "40658" "40664"
## [64] "40665" "40668" "40685" "40694" "40720" "40728" "40730" "40738" "40743"
## [73] "40750" "40767" "40768" "40769" "40773" "40775" "40777" "40779" "40784"
## [82] "40803" "40855" "40861" "40876" "40878"
d2 <- merge(d, ccvol, by = "record_id")
alpha = 0.05
oa_data <- filter(d2, Group == "Older Adults")
oa_cor_data <- select(oa_data, IS:RA, actamp:fact, matches("CC_"))
oa_cor_data <- oa_cor_data[complete.cases(oa_cor_data),]
oa_mat <- cor(oa_cor_data)
oa_res <- cor.mtest(oa_mat, conf.level = (1-alpha))
corrplot(oa_mat, p.mat = oa_res$p, sig.level = alpha, insig = "blank", type = "upper")
ya_data <- filter(d2, Group == "Young Adults")
ya_cor_data <- select(ya_data, IS:RA, actamp:fact, matches("CC_"))
ya_cor_data <- ya_cor_data[complete.cases(ya_cor_data),]
ya_mat <- cor(ya_cor_data)
ya_res <- cor.mtest(ya_mat, conf.level = (1-alpha))
corrplot(ya_mat, p.mat = ya_res$p, sig.level = alpha, insig = "blank", type = "upper")
all_cor_data <- select(d2, IS:RA, actamp:fact, matches("CC_"))
all_cor_data <- all_cor_data[complete.cases(all_cor_data),]
all_mat <- cor(all_cor_data)
all_res <- cor.mtest(all_mat, conf.level = (1-alpha))
corrplot(all_mat, p.mat = all_res$p, sig.level = alpha, insig = "blank", type = "upper")
alpha = 0.05
oa_data <- filter(d2, Group == "Older Adults")
oa_cor_data <- select(oa_data, matches("zscore|z_score"), matches("CC_"))
oa_mat <- cor(oa_cor_data)
oa_res <- cor.mtest(oa_mat, conf.level = (1-alpha))
corrplot(oa_mat, p.mat = oa_res$p, sig.level = alpha, insig = "blank", type = "upper")
stepdata <- select(oa_data, age, IS:RA, actalph:fact, -actwidthratio, CC_Total)
library(beset)
mod_fs1 <- beset_lm(CC_Total ~ ., data = stepdata, n_folds = 38)#, force_in = "age")
## Performing leave-one-out cross-validation
## NOTE: Repetitions of leave-one-out cross-validation
## are pointless and will not be performed.
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
summary(mod_fs1, n_folds = 38) #age
##
## =======================================================
## Best Model:
## ~ age
##
## Coefficients:
## Estimate
## (Intercept) 5610.00
## age -34.64
##
## (Dispersion parameter for gaussian family taken to be 131913.6)
##
## Log-likelihood: -276.9 on 3 Df
## AIC: 559.8
##
## Number of Fisher Scoring iterations: 2
##
## Train-sample R-squared = 0.25
## Cross-validated R-squared = 0.15
## =======================================================
summary(mod_fs1, n_folds = 38, oneSE = FALSE) #age, RA, actalph
##
## =======================================================
## Best Model:
## ~ age + RA + actalph
##
## Coefficients:
## Estimate
## (Intercept) 6518.00
## age -32.71
## RA -775.10
## actalph 874.30
##
## (Dispersion parameter for gaussian family taken to be 121656)
##
## Log-likelihood: -274.3 on 5 Df
## AIC: 558.55
##
## Number of Fisher Scoring iterations: 2
##
## Train-sample R-squared = 0.35
## Cross-validated R-squared = 0.2
## =======================================================
plot(mod_fs1, n_folds = 38, oneSE = FALSE)
Best model without 1 SE rule - age (S), RA, width (S) predict total CC volume
summary(lm(CC_Total ~ age + RA + actalph, data = oa_data))
##
## Call:
## lm(formula = CC_Total ~ age + RA + actalph, data = oa_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -727.73 -209.44 -39.86 217.81 752.34
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6518.387 826.750 7.884 3.51e-09 ***
## age -32.707 9.654 -3.388 0.00179 **
## RA -775.059 524.124 -1.479 0.14841
## actalph 874.332 425.083 2.057 0.04744 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 348.8 on 34 degrees of freedom
## Multiple R-squared: 0.3468, Adjusted R-squared: 0.2892
## F-statistic: 6.018 on 3 and 34 DF, p-value: 0.002108
oa_data %>%
ggplot() +
geom_point(aes(x = actalph, y = CC_Total)) +
stat_smooth(aes(x = actalph, y = CC_Total), method = "lm") +
scale_color_manual(values = c("blue")) +
xlab("Width (alpha)") + ylab("Corpus Callosum Volume")
#No significant interaction effect
summary(lm(CC_Total ~ IS * Group, data = d2))
##
## Call:
## lm(formula = CC_Total ~ IS * Group, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1176.99 -270.15 29.94 243.05 1023.88
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3925.2 226.3 17.343 <2e-16 ***
## IS -265.4 561.5 -0.473 0.6380
## GroupOlder Adults -631.6 334.6 -1.888 0.0633 .
## IS:GroupOlder Adults 180.7 746.7 0.242 0.8095
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 410 on 69 degrees of freedom
## Multiple R-squared: 0.3399, Adjusted R-squared: 0.3112
## F-statistic: 11.85 on 3 and 69 DF, p-value: 2.391e-06
summary(lm(CC_Total ~ IV * Group, data = d2))
##
## Call:
## lm(formula = CC_Total ~ IV * Group, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1166.45 -247.79 50.13 225.23 1025.53
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3718.58 242.38 15.342 <2e-16 ***
## IV 119.17 264.13 0.451 0.6533
## GroupOlder Adults -572.59 340.54 -1.681 0.0972 .
## IV:GroupOlder Adults 15.51 392.13 0.040 0.9686
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 409.5 on 69 degrees of freedom
## Multiple R-squared: 0.3415, Adjusted R-squared: 0.3129
## F-statistic: 11.93 on 3 and 69 DF, p-value: 2.206e-06
summary(lm(CC_Total ~ RA * Group, data = d2))
##
## Call:
## lm(formula = CC_Total ~ RA * Group, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1147.09 -242.33 20.27 259.65 1051.30
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4033.4 346.0 11.656 <2e-16 ***
## RA -255.3 412.2 -0.619 0.538
## GroupOlder Adults -421.8 610.6 -0.691 0.492
## RA:GroupOlder Adults -165.2 715.1 -0.231 0.818
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 408.1 on 69 degrees of freedom
## Multiple R-squared: 0.3461, Adjusted R-squared: 0.3176
## F-statistic: 12.17 on 3 and 69 DF, p-value: 1.748e-06
summary(lm(CC_Total ~ fact * Group, data = d2))
##
## Call:
## lm(formula = CC_Total ~ fact * Group, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1094.21 -293.86 17.76 250.17 1171.73
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3874.95673 164.15259 23.606 < 2e-16 ***
## fact -0.01331 0.03854 -0.345 0.73081
## GroupOlder Adults -853.16378 233.11524 -3.660 0.00049 ***
## fact:GroupOlder Adults 0.06490 0.05136 1.264 0.21060
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 403.7 on 69 degrees of freedom
## Multiple R-squared: 0.36, Adjusted R-squared: 0.3322
## F-statistic: 12.94 on 3 and 69 DF, p-value: 8.448e-07
summary(lm(CC_Central ~ fact * Group, data = d2))
##
## Call:
## lm(formula = CC_Central ~ fact * Group, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -244.000 -80.220 -9.062 64.287 274.186
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.806e+02 4.441e+01 13.074 < 2e-16 ***
## fact 1.946e-02 1.043e-02 1.867 0.06621 .
## GroupOlder Adults -1.749e+02 6.307e+01 -2.774 0.00712 **
## fact:GroupOlder Adults -6.459e-03 1.389e-02 -0.465 0.64350
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 109.2 on 69 degrees of freedom
## Multiple R-squared: 0.4727, Adjusted R-squared: 0.4498
## F-statistic: 20.62 on 3 and 69 DF, p-value: 1.204e-09
#Significant age group effect
summary(lm(CC_Total ~ IS + Group, data = d2))
##
## Call:
## lm(formula = CC_Total ~ IS + Group, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1182.65 -288.37 46.67 243.44 1012.72
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3886.0 157.0 24.756 < 2e-16 ***
## IS -163.2 367.7 -0.444 0.658
## GroupOlder Adults -554.5 102.0 -5.435 7.54e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 407.3 on 70 degrees of freedom
## Multiple R-squared: 0.3394, Adjusted R-squared: 0.3205
## F-statistic: 17.98 on 2 and 70 DF, p-value: 4.991e-07
summary(lm(CC_Total ~ IV + Group, data = d2))
##
## Call:
## lm(formula = CC_Total ~ IV + Group, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1166.73 -249.09 49.88 221.73 1026.18
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3712.39 183.79 20.199 < 2e-16 ***
## IV 126.21 193.83 0.651 0.517
## GroupOlder Adults -559.68 96.72 -5.786 1.86e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 406.6 on 70 degrees of freedom
## Multiple R-squared: 0.3415, Adjusted R-squared: 0.3227
## F-statistic: 18.15 on 2 and 70 DF, p-value: 4.458e-07
summary(lm(CC_Total ~ RA + Group, data = d2))
##
## Call:
## lm(formula = CC_Total ~ RA + Group, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1153.33 -244.63 17.54 265.94 1047.26
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4078.54 283.60 14.381 < 2e-16 ***
## RA -310.19 334.56 -0.927 0.357
## GroupOlder Adults -561.03 95.52 -5.873 1.31e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 405.3 on 70 degrees of freedom
## Multiple R-squared: 0.3456, Adjusted R-squared: 0.3269
## F-statistic: 18.48 on 2 and 70 DF, p-value: 3.592e-07
summary(lm(CC_Total ~ fact + Group, data = d2))
##
## Call:
## lm(formula = CC_Total ~ fact + Group, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1136.4 -309.6 38.5 274.7 1097.1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3733.39115 120.49458 30.984 < 2e-16 ***
## fact 0.02323 0.02558 0.908 0.367
## GroupOlder Adults -584.62071 96.23509 -6.075 5.78e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 405.4 on 70 degrees of freedom
## Multiple R-squared: 0.3452, Adjusted R-squared: 0.3265
## F-statistic: 18.45 on 2 and 70 DF, p-value: 3.655e-07
summary(lm(CC_Central ~ fact + Group, data = d2))
##
## Call:
## lm(formula = CC_Central ~ fact + Group, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -246.15 -72.85 -11.97 63.99 270.79
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.947e+02 3.228e+01 18.424 < 2e-16 ***
## fact 1.582e-02 6.853e-03 2.309 0.0239 *
## GroupOlder Adults -2.017e+02 2.578e+01 -7.823 3.87e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 108.6 on 70 degrees of freedom
## Multiple R-squared: 0.4711, Adjusted R-squared: 0.4559
## F-statistic: 31.17 on 2 and 70 DF, p-value: 2.085e-10
summary(lm(CC_Mid_Anterior ~ fact, data = d2[d2$Group == "Older Adults",])) #NS
##
## Call:
## lm(formula = CC_Mid_Anterior ~ fact, data = d2[d2$Group == "Older Adults",
## ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -187.220 -57.985 -3.417 44.276 248.820
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.010e+02 3.583e+01 11.192 2.82e-13 ***
## fact 1.024e-02 7.349e-03 1.394 0.172
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 87.39 on 36 degrees of freedom
## Multiple R-squared: 0.05118, Adjusted R-squared: 0.02483
## F-statistic: 1.942 on 1 and 36 DF, p-value: 0.172
summary(lm(CC_Mid_Anterior ~ fact + age, data = d2[d2$Group == "Older Adults",])) #NS
##
## Call:
## lm(formula = CC_Mid_Anterior ~ fact + age, data = d2[d2$Group ==
## "Older Adults", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -112.37 -52.23 1.03 36.13 235.25
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 895.035485 162.536047 5.507 3.45e-06 ***
## fact 0.005010 0.006813 0.735 0.4670
## age -6.915642 2.230121 -3.101 0.0038 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 78.5 on 35 degrees of freedom
## Multiple R-squared: 0.2557, Adjusted R-squared: 0.2132
## F-statistic: 6.011 on 2 and 35 DF, p-value: 0.005699
summary(lm(CC_Central ~ fact, data = d2[d2$Group == "Older Adults",])) # p = 0.06
##
## Call:
## lm(formula = CC_Central ~ fact, data = d2[d2$Group == "Older Adults",
## ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -124.548 -51.769 -8.831 22.865 180.833
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.057e+02 3.285e+01 12.35 1.67e-14 ***
## fact 1.300e-02 6.739e-03 1.93 0.0616 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 80.13 on 36 degrees of freedom
## Multiple R-squared: 0.09373, Adjusted R-squared: 0.06856
## F-statistic: 3.723 on 1 and 36 DF, p-value: 0.06157
summary(lm(CC_Central ~ fact + age, data = d2[d2$Group == "Older Adults",])) #NS
##
## Call:
## lm(formula = CC_Central ~ fact + age, data = d2[d2$Group == "Older Adults",
## ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -105.37 -61.75 -10.31 27.42 204.04
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 649.794432 162.918364 3.988 0.000323 ***
## fact 0.010418 0.006829 1.525 0.136140
## age -3.417460 2.235366 -1.529 0.135299
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 78.68 on 35 degrees of freedom
## Multiple R-squared: 0.1505, Adjusted R-squared: 0.1019
## F-statistic: 3.099 on 2 and 35 DF, p-value: 0.05764
summary(lm(CC_Central ~ fact, data = d2[d2$Group == "Young Adults",])) #NS
##
## Call:
## lm(formula = CC_Central ~ fact, data = d2[d2$Group == "Young Adults",
## ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -244.00 -117.13 -14.46 96.19 274.19
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 580.60337 54.45826 10.661 3.18e-12 ***
## fact 0.01946 0.01279 1.522 0.137
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 133.9 on 33 degrees of freedom
## Multiple R-squared: 0.0656, Adjusted R-squared: 0.03729
## F-statistic: 2.317 on 1 and 33 DF, p-value: 0.1375
summary(lm(CC_Central ~ fact + age, data = d2[d2$Group == "Young Adults",])) #NS
##
## Call:
## lm(formula = CC_Central ~ fact + age, data = d2[d2$Group == "Young Adults",
## ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -244.47 -115.92 -12.71 94.44 271.58
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 603.77570 131.55795 4.589 6.53e-05 ***
## fact 0.02010 0.01339 1.501 0.143
## age -1.18107 6.08493 -0.194 0.847
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 135.9 on 32 degrees of freedom
## Multiple R-squared: 0.0667, Adjusted R-squared: 0.008372
## F-statistic: 1.144 on 2 and 32 DF, p-value: 0.3314
#Significant effect of age
summary(lm(CC_Total ~ actalph, data = oa_data)) #p = 0.06
##
## Call:
## lm(formula = CC_Total ~ actalph, data = oa_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1162.80 -165.11 -34.94 178.15 959.06
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3610.6 211.3 17.087 <2e-16 ***
## actalph 826.9 464.4 1.781 0.0834 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 402.1 on 36 degrees of freedom
## Multiple R-squared: 0.08093, Adjusted R-squared: 0.0554
## F-statistic: 3.17 on 1 and 36 DF, p-value: 0.08343
summary(lm(CC_Total ~ actalph+age, data = oa_data)) #but effect goes away when include age
##
## Call:
## lm(formula = CC_Total ~ actalph + age, data = oa_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -770.54 -219.30 -45.58 241.32 739.99
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5790.993 675.671 8.571 4.08e-10 ***
## actalph 683.646 411.862 1.660 0.10587
## age -32.950 9.815 -3.357 0.00191 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 354.7 on 35 degrees of freedom
## Multiple R-squared: 0.3048, Adjusted R-squared: 0.2651
## F-statistic: 7.673 on 2 and 35 DF, p-value: 0.001725
#from corplots
summary(lm(CC_Mid_Anterior ~ actalph + age, data = oa_data)) #NS
##
## Call:
## lm(formula = CC_Mid_Anterior ~ actalph + age, data = oa_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -117.51 -48.46 -13.07 38.61 220.10
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 979.206 146.406 6.688 9.68e-08 ***
## actalph 128.841 89.243 1.444 0.15771
## age -7.004 2.127 -3.293 0.00227 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 76.85 on 35 degrees of freedom
## Multiple R-squared: 0.2867, Adjusted R-squared: 0.2459
## F-statistic: 7.033 on 2 and 35 DF, p-value: 0.002708
summary(lm(CC_Central ~ actalph + age, data = oa_data)) #NS
##
## Call:
## lm(formula = CC_Central ~ actalph + age, data = oa_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -112.35 -47.05 -16.64 28.33 193.78
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 782.021 151.976 5.146 1.03e-05 ***
## actalph 106.263 92.638 1.147 0.2591
## age -3.999 2.208 -1.812 0.0786 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 79.77 on 35 degrees of freedom
## Multiple R-squared: 0.1268, Adjusted R-squared: 0.07691
## F-statistic: 2.541 on 2 and 35 DF, p-value: 0.0932
summary(lm(CC_Total ~ actwidthratio, data = ya_data)) #NS
##
## Call:
## lm(formula = CC_Total ~ actwidthratio, data = ya_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -762.72 -287.87 31.78 296.50 793.36
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4253.0 718.8 5.917 1.23e-06 ***
## actwidthratio -649.8 1082.4 -0.600 0.552
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 399 on 33 degrees of freedom
## Multiple R-squared: 0.0108, Adjusted R-squared: -0.01917
## F-statistic: 0.3604 on 1 and 33 DF, p-value: 0.5524
summary(lm(CC_Total ~ actbeta, data = ya_data)) #NS
##
## Call:
## lm(formula = CC_Total ~ actbeta, data = ya_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -753.13 -319.56 32.59 288.69 763.98
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3785.642 183.231 20.661 <2e-16 ***
## actbeta 5.771 26.030 0.222 0.826
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 400.8 on 33 degrees of freedom
## Multiple R-squared: 0.001487, Adjusted R-squared: -0.02877
## F-statistic: 0.04915 on 1 and 33 DF, p-value: 0.8259
Predictors of relative response time and false starts?
pvt <- read_csv("~/Box/CogNeuroLab/Aging Decision Making R01/Analysis/pvt/pvt_stats_2019-12-11.csv")
## Parsed with column specification:
## cols(
## record_id = col_double(),
## rt_mean = col_double(),
## rt_sd = col_double(),
## fs = col_double(),
## rl = col_double()
## )
head(pvt)
## # A tibble: 6 x 5
## record_id rt_mean rt_sd fs rl
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 30003 270. 75.3 2 0
## 2 30004 315. 166. 2 0
## 3 30008 295. 85.3 1 0
## 4 30008 327. 76.8 0 0
## 5 30009 321. 209. 0 0
## 6 30012 290. 61.5 1 0
d2 <- merge(d2, pvt, by = "record_id")
oa_data <- filter(d2, Group == "Older Adults")
library(beset)
stepdata <- select(oa_data, age, IS:RA, actalph:fact, -actwidthratio, matches("fa_mean|md_mean|CC_"), rt_mean)
mod_rt1 <- beset_lm(rt_mean ~ ., data = stepdata, n_folds = 38)#, force_in = "age")
## Warning in check_lindep(data): Found 1 linear dependency. Removed the following predictor:
## CC_Total
## Performing leave-one-out cross-validation
## NOTE: Repetitions of leave-one-out cross-validation
## are pointless and will not be performed.
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
summary(mod_rt1, n_folds = 38) #NS
##
## =======================================================
## Best Model:
## ~ 1
##
## Coefficients:
## Estimate
## (Intercept) 340.8
##
## (Dispersion parameter for gaussian family taken to be 72423.21)
##
## Log-likelihood: -266 on 2 Df
## AIC: 536.06
##
## Number of Fisher Scoring iterations: 2
##
## Train-sample R-squared = 0
## Cross-validated R-squared = -0.05
## =======================================================
summary(mod_rt1, n_folds = 38, oneSE = FALSE) #NS
##
## =======================================================
## Best Model:
## ~ 1
##
## Coefficients:
## Estimate
## (Intercept) 340.8
##
## (Dispersion parameter for gaussian family taken to be 72423.21)
##
## Log-likelihood: -266 on 2 Df
## AIC: 536.06
##
## Number of Fisher Scoring iterations: 2
##
## Train-sample R-squared = 0
## Cross-validated R-squared = -0.05
## =======================================================
plot(mod_rt1, n_folds = 38, oneSE = FALSE)
stepdata <- select(oa_data, age, IS:RA, actalph:fact, -actwidthratio, matches("fa_mean|md_mean|CC_"), fs)
mod_fs1 <- beset_lm(fs ~ ., data = stepdata, n_folds = 38)#, force_in = "age")
## Warning in check_lindep(data): Found 1 linear dependency. Removed the following predictor:
## CC_Total
## Performing leave-one-out cross-validation
## NOTE: Repetitions of leave-one-out cross-validation
## are pointless and will not be performed.
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
## Performing leave-one-out cross-validation
summary(mod_fs1, n_folds = 38)
##
## =======================================================
## Best Model:
## ~ 1
##
## Coefficients:
## Estimate
## (Intercept) 3.132
##
## (Dispersion parameter for gaussian family taken to be 44.11735)
##
## Log-likelihood: -125.4 on 2 Df
## AIC: 254.73
##
## Number of Fisher Scoring iterations: 2
##
## Train-sample R-squared = 0
## Cross-validated R-squared = -0.05
## =======================================================
summary(mod_fs1, n_folds = 38, oneSE = FALSE)
##
## =======================================================
## Best Model:
## ~ 1
##
## Coefficients:
## Estimate
## (Intercept) 3.132
##
## (Dispersion parameter for gaussian family taken to be 44.11735)
##
## Log-likelihood: -125.4 on 2 Df
## AIC: 254.73
##
## Number of Fisher Scoring iterations: 2
##
## Train-sample R-squared = 0
## Cross-validated R-squared = -0.05
## =======================================================
plot(mod_fs1, n_folds = 38, oneSE = FALSE)